# BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer

arXiv: https://arxiv.org/abs/2212.05855 · HTML: https://mlsanity.com/paper/2212.05855/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer](https://mlsanity.com/paper/1909.06956/) (2019)
- [LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup](https://mlsanity.com/paper/1904.11272/) (2019)
- [Multi-Head Attention with Disagreement Regularization](https://mlsanity.com/paper/1810.10183/) (2018)
- [Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer](https://mlsanity.com/paper/2104.01867/) (2021)
- [EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer](https://mlsanity.com/paper/2207.09840/) (2022)
- [SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer](https://mlsanity.com/paper/2104.10567/) (2021)
- [SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal](https://mlsanity.com/paper/2112.03631/) (2021)

## Built on by (most cited first, 16 of 16)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model](https://mlsanity.com/paper/2403.07764/) (2024)
- [Toward Tiny and High-quality Facial Makeup with Data Amplify Learning](https://mlsanity.com/paper/2403.15033/) (2024)
- [Makeup Prior Models for 3D Facial Makeup Estimation and Applications](https://mlsanity.com/paper/2403.17761/) (2024)
- [MAD: Makeup All-in-One with Cross-Domain Diffusion Model](https://mlsanity.com/paper/2504.02545/) (2025)
- [FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer](https://mlsanity.com/paper/2508.05069/) (2025)
- [BeautyBank: Encoding Facial Makeup in Latent Space](https://mlsanity.com/paper/2411.11231/) (2024)
- [AvatarMakeup: Realistic Makeup Transfer for 3D Animatable Head Avatars](https://mlsanity.com/paper/2507.02419/) (2025)
- [EvoMakeup: High-Fidelity and Controllable Makeup Editing with MakeupQuad](https://mlsanity.com/paper/2508.05994/) (2025)
- [FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles](https://mlsanity.com/paper/2508.03241/) (2025)
- [Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas](https://mlsanity.com/paper/2404.13944/) (2024)
- [DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation](https://mlsanity.com/paper/2507.13292/) (2025)
- [Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation](https://mlsanity.com/paper/2509.02445/) (2025)
- [Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features](https://mlsanity.com/paper/2603.20012/) (2026)
- [From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data](https://mlsanity.com/paper/2605.07861/) (2026)
- [MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer](https://mlsanity.com/paper/2607.20924/) (2026)

## Abstract (arXiv)

In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global attention, simply concatenate features, or implicitly manipulate features in latent space, we propose a component-specific correspondence to directly transfer the makeup style of a reference image to the corresponding components (e.g., skin, lips, eyes) of a source image, making elaborate and accurate local makeup transfer. As an auxiliary, the long-range visual dependencies of Transformer are introduced for effective global makeup transfer. Instead of the commonly used cycle structure that is complex and unstable, we employ a content consistency loss coupled with a content encoder to implement efficient single-path makeup transfer. The key insights of this study are modeling component-specific correspondence for local makeup transfer, capturing long-range dependencies for global makeup transfer, and enabling efficient makeup transfer via a single-path structure. We also contribute BeautyFace, a makeup transfer dataset to supplement existing datasets. This dataset contains 3,000 faces, covering more diverse makeup styles, face poses, and races. Each face has annotated parsing map. Extensive experiments demonstrate the effectiveness of our method against state-of-the-art methods. Besides, our method is appealing as it is with only 1M parameters, outperforming the state-of-the-art methods (BeautyGAN: 8.43M, PSGAN: 12.62M, SCGAN: 15.30M, CPM: 9.24M, SSAT: 10.48M).